In [ ]:
plt.figure(figsize=(15, 10))
plt.title("Online Food Order Decisions Based on the Age of the Customer")
sns.histplot(x="Age", hue="Output", data=data)
plt.show()
Setting Up:
We'll begin by bringing in (importing) the tools (libraries) we'll need from Python and loading the data set.
import numpy as np
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme(style="whitegrid")
data = pd.read_csv("onlinefoods.csv")
print(data.head())
import plotly.io as pio
pio.renderers.default = 'notebook'
Age Gender Marital Status Occupation Monthly Income \ 0 20 Female Single Student No Income 1 24 Female Single Student Below Rs.10000 2 22 Male Single Student Below Rs.10000 3 22 Female Single Student No Income 4 22 Male Single Student Below Rs.10000 Educational Qualifications Family size latitude longitude Pin code \ 0 Post Graduate 4 12.9766 77.5993 560001 1 Graduate 3 12.9770 77.5773 560009 2 Post Graduate 3 12.9551 77.6593 560017 3 Graduate 6 12.9473 77.5616 560019 4 Post Graduate 4 12.9850 77.5533 560010 Output Feedback Unnamed: 12 0 Yes Positive Yes 1 Yes Positive Yes 2 Yes Negative Yes 3 Yes Positive Yes 4 Yes Positive Yes
Exploring the Data:
This data set includes details like a customer's age, marital status, job, monthly income, education level, family size, location (latitude and longitude), home postal code, whether they ordered again, and feedback from their last order (positive or negative). Let's take a closer look at the information for each category (column) in the data set.
print(data.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 388 entries, 0 to 387 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 388 non-null int64 1 Gender 388 non-null object 2 Marital Status 388 non-null object 3 Occupation 388 non-null object 4 Monthly Income 388 non-null object 5 Educational Qualifications 388 non-null object 6 Family size 388 non-null int64 7 latitude 388 non-null float64 8 longitude 388 non-null float64 9 Pin code 388 non-null int64 10 Output 388 non-null object 11 Feedback 388 non-null object 12 Unnamed: 12 388 non-null object dtypes: float64(2), int64(3), object(8) memory usage: 39.5+ KB None
Analyzing Online Food Ordering Trends:
Now, we'll dive into analyzing this data. First, we'll examine online food ordering decisions based on a customer's age.
plt.figure(figsize=(15, 10))
plt.title("Online Food Order Decisions Based on the Age of the Customer")
sns.histplot(x="Age", hue="Output", data=data)
plt.show()
The data shows that the 22-25 age group orders the most frequently. This suggests they're a target market for online food delivery companies.
Next, let's look at online food ordering decisions based on family size.
plt.figure(figsize=(15,10))
plt.title("Online Food Order Decisions Based on the Size of the Family")
sns.histplot(x='Family size', hue='Output', data=data)
plt.show()
Families of 2 and 3 members order food the most often. These could be roommates, couples, or small families.
Focusing on Repeat Customers: Let's create a new data set that only includes customers who ordered again
buying_again_data = data.query("Output == 'Yes'")
print(buying_again_data.head())
Age Gender Marital Status Occupation Monthly Income \ 0 20 Female Single Student No Income 1 24 Female Single Student Below Rs.10000 2 22 Male Single Student Below Rs.10000 3 22 Female Single Student No Income 4 22 Male Single Student Below Rs.10000 Educational Qualifications Family size latitude longitude Pin code \ 0 Post Graduate 4 12.9766 77.5993 560001 1 Graduate 3 12.9770 77.5773 560009 2 Post Graduate 3 12.9551 77.6593 560017 3 Graduate 6 12.9473 77.5616 560019 4 Post Graduate 4 12.9850 77.5533 560010 Output Feedback Unnamed: 12 0 Yes Positive Yes 1 Yes Positive Yes 2 Yes Negative Yes 3 Yes Positive Yes 4 Yes Positive Yes
Gender and Repeat Orders:
Now, let's look at the gender data. We want to find out who orders food online more often.
gender = buying_again_data["Gender"].value_counts()
label = gender.index
counts = gender.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label,values=counts)])
fig.update_layout(title_text='Who Orders Food Online More: Male Vs. Female')
fig.update_traces(hoverinfo='label+percent', textinfo = 'value', textfont_size = 30,marker = dict(colors=colors, line = dict(color='black', width=3)))
fig.show()
Based on the data, male customers seem to order more compared to females.
Marital Status and Repeat Orders:
Now, let's examine the marital status of customers who ordered again.
marital = buying_again_data["Marital Status"].value_counts()
label = marital.index
counts = marital.values
colors = ['gold', 'lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Who orders Food Online More: Married Vs Singles')
fig.update_traces(hoverinfo='label+percent', textinfo = 'value',textfont_size =30, marker = dict(colors=colors, line=dict(color ='black',width=3 )))
fig.show()
The graph shows that 76.1% of frequent customers are single.
Income and Repeat Orders:
Let's see what the income bracket is for customers who ordered again.
income = buying_again_data["Monthly Income"].value_counts()
label = income.index
counts = income.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values = counts)])
fig.update_layout(title_text="Which Income Group Orders Food Online More")
fig.update_traces(hoverinfo='label+percent', textinfo="value", textfont_size=30,marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
According to the graph, 54% of these customers don't fall under any income group. They could be stay-at-home parents or students.
Preparing for Machine Learning:
Now, we'll get the data ready to train a machine learning model. This involves changing all the descriptive categories (categorical features) into numerical values.
data['Gender'] = data["Gender"].map({"Male":1, "Female":0})
data["Marital Status"] = data["Marital Status"].map({"Married":2, "Single":1, "Prefer not to say": 0})
data["Occupation"] = data["Occupation"].map({"Student":1, "Employee":2,"Self Employeed":3, "House Wife":4})
data["Educational Qualifications"] = data["Educational Qualifications"].map({"Graduate":1, "Post Graduate":2, "Ph.D":3, "School":4, "Uneducated":5})
data["Monthly Income"] = data["Monthly Income"].map({"No Income": 0,"25001 to 50000":5000, "More than 50000":7000, "10001 to 25000":25000, "Below Rs .10000": 10000})
data["Feedback"] = data['Feedback'].map({"Positive":1, "Negative":0})
print(data.head())
Age Gender Marital Status Occupation Monthly Income \ 0 20 0 1 1.0 0.0 1 24 0 1 1.0 NaN 2 22 1 1 1.0 NaN 3 22 0 1 1.0 0.0 4 22 1 1 1.0 NaN Educational Qualifications Family size latitude longitude Pin code \ 0 2 4 12.9766 77.5993 560001 1 1 3 12.9770 77.5773 560009 2 2 3 12.9551 77.6593 560017 3 1 6 12.9473 77.5616 560019 4 2 4 12.9850 77.5533 560010 Output Feedback Unnamed: 12 0 Yes 1.0 Yes 1 Yes 1.0 Yes 2 Yes NaN Yes 3 Yes 1.0 Yes 4 Yes 1.0 Yes
Building a Prediction Model:
Online Food Order Prediction Model:
from sklearn.model_selection import train_test_split
x = np.array(data[["Age","Gender","Marital Status","Occupation","Monthly Income","Educational Qualifications","Family size","Pin code","Feedback"]])
y = np.array(data[["Output"]])
from sklearn.ensemble import RandomForestClassifier
xtrain,xtest,ytrain,ytest =train_test_split(x,y,test_size=0.10,random_state=42)
model = RandomForestClassifier()
model.fit(xtrain,ytrain)
print(model.score(xtest,ytest))
0.9230769230769231
C:\Users\okoro\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\sklearn\base.py:1474: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
Now, let's create a form where users can enter customer data, and the model will predict whether the customer will order food again.
print("Enter Customer Detail to Predict If Customer Will Order Again")
a = int(input("Enter the Age of the Customer: "))
b = int(input("Enter the Gender of the Customer(1 = Male, 0 = Female): "))
c = int(input("Marital Status of the Customer(1 = Sinagle, 2 = Married, 3 = Not Revealed)"))
d = int(input("Occupation of the Customer (Student = 1, Employee = 2, Self Employeed = 3, House wife = 4)"))
e = int(input("Monthly Income: "))
f = int(input("Educational Qualification (Graduate = 1, Post Graduate = 2, Ph.D = 3, School = 4, Uneducated = 5): "))
g = int(input("Family Size: "))
h = int(input("Pin Code: "))
i = int(input("Review of the Last Order (1 = Positive, 0 = Negetive: "))
features = np.array([[a,b,c,d,e,f,g,h,i]])
print("Finding if the customer will order again: ", model.predict(features))
Enter Customer Detail to Predict If Customer Will Order Again Finding if the customer will order again: ['Yes']
Conclusion:
I hope you enjoyed this aexploration on Online Food Delivery Prediction with Machine Learning. Feel free to ask any questions you may have in the comments below.